Papilledema - a specific type of optic nerve edema associated with elevated intracranial pressure - occurs in veterans due to intracranial masses or hemorrhage, obstructive hydrocephalus, following traumatic brain injury, and from idiopathic causes. Currently, the diagnosis of papilledema and its severity are assessed by direct funduscopic observation or digital fundus photographs using a six-stage grading scale. The scale is qualitative, categorical, subject to inter-rater variability, requires clinical expertise, and does not distinguish papilledea from other causes of optic nerve edema. Recent developments in the automated image-analysis of optical coherence tomography (OCT) images have led to the availability of continuous quantitative measures (such as the total retinal volume) of edema severity. However, optical coherence tomography is still too expensive to become readily available outside specialized ophthalmology clinics. Use of low-cost portable color fundus cameras is a more economically viable imaging option in telemedical and emergency settings, but there currently exists no objective method of analyzing and classifying the cause and severity of optic nerve edema. The overall objective of this project is to develop automated color-fundus-photograph-based image-analysis strategies to rapidly and accurately determine the severity of optic nerve edema and to ascertain features that differentiate its cause. The central hypothesis is that, through the use of optical-coherence-tomgraphy-based severity measures as a reference standard for computer-based learning of color-fundus-photograph features, the ability to automatically estimate the severity of optic nerve edema in telemedical settings (where only color fundus photographs are likely to be available) will be improved. The rationale for the proposed research is that having automated methods for determination of the severity and cause of optic nerve edema from fundus photographs will enable earlier diagnosis of vision- and life-threatening conditions in telemedical and emergency settings in veterans. The following specific aims will be pursued: 1. Develop and evaluate the automated methodology for computing swollen-optic-nerve-head- relevant features from color fundus photographs. This will be completed by refining and augmenting automated fundus-image-analysis approaches for computation of global/regional vascular, textural, and optic-disc boundary features in patients with optic nerve edema. The relevance of these features will first be evaluated in this aim via their associations with expert defined severity rankings. 2. Identify features from color fundus photographs that optimally correlate with OCT-based measurements of severity in patients with optic nerve edema and develop a fundus-photograph- based continuous severity scale. This will be accomplished by using machine-learning approaches to relate global/regional fundus-based features to recently developed novel automated OCT-based measures of severity (e.g., the total retinal volume). It is expected that fundus-based severity measures using the developed automated approach will better correlate (than expert-defined Frisn scale grades - the current clinical fundus-based ordinal severity scale) with OCT-based measures of severity. 3. Identify features from color fundus photographs that differentiate papilledema from other causes of optic disc swelling and develop a predictive classifier. The working hypothesis is that regional differences around the optic disc will contribute the most in the automatic differentiation process. The approach is innovative because the use of automated image-analysis algorithms for assessing the extent and cause of optic nerve edema in low-cost images reflects a significant department from the status quo of relying on qualitative information. The proposed research is significant because it will help to establish much- needed quantitative and objective methods by which to assess the severity and cause of optic disc swelling of veterans in telemedical and emergency settings.